Modified Propagated Last Labeling System and Method for Connected Components

ABSTRACT

Embodiments disclosed include methods and systems for assigning one or more labels to one or more segments of data received in an incoming segment to a line buffer for propagated component labeling, including preventing repeated labels in each line of the line buffer by assigning a different label for each of the one or more segments of data received in each line; labeling the incoming segment of the one or more segments of data by adopting a label of an overlapping segment on a prior received line when the overlapping segment does not overlap any other segment of data; labeling the incoming segment of the one or more segments of data by adopting a label of an overlapping segment on a prior received line when the overlapping segment overlaps more than one segment on the incoming segment when the segment is a first segment in the line buffer; and labeling the incoming segment of the one or more segments of data by adopting a label of a last overlapping segment when more than one segment overlaps the incoming segment.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application constitutes a divisional application of U.S.patent application Ser. No. 12/030,003 filed Feb. 12, 2008, which is acontinuation-in-part of U.S. patent application Ser. No. 12/028,146,filed Feb. 8, 2008, now U.S. Pat. 8,249,348 which is acontinuation-in-part of U.S. patent application Ser. No. 12/025,738,filed Feb. 4, 2008, now U.S. Pat. No. 8,280,167. All subject matter ofthe Related Applications and of any and all parent, grandparent,great-grandparent, etc. applications of the Related Applications isincorporated herein by reference to the extent such subject matter isnot inconsistent herewith.

BACKGROUND

In general, connecting related data and maintaining an accounting ofrelated data connections is referred to as “connected componentlabeling” herein referred to as “CCL”. CCL is typically used for imageanalysis for computer vision. For example, an algorithm can be appliedto an image, such as a binary image to separate object pixels frombackground pixels. Another use of CCL is to provide numeric labels forcomponents identified in an image, such as a two-dimensional (2D) array.CCL is also used during the segmentation of other types of 1D, 2D, and3D data such as financial data and digital audio. CCL and segmentationextract the needed information from data and images so that digitalcommunications such as computer networks are not clogged withunnecessary high-bandwidth data.

Known methods to determine CCL include scanning an image to assign aprovisional label and later determine a final label for each pixel.Scanning can assign such labels by locating neighbors and determining anappropriate label. Known methods include applying multi-pass labeling,two-pass labeling, depending on the complexity required for anapplication.

A problem with CCL methods is that memory requirements for manyapplications do not permit the required use of space for known CCLtechniques. For example, the multi-pass labeling method requiresrepeated scanning and saving data in memory prior to determining a finallabel value for a single pixel in an image. What is needed is a CCLsystem and method that does not require the memory space of earlierknown techniques and demands less bandwidth on networks.

SUMMARY

Embodiments disclosed include methods for assigning one or more labelsto one or more segments of data received in an incoming segment to aline buffer for propagated component labeling, including preventingrepeated labels in each line of the line buffer by assigning a differentlabel for each of the one or more segments of data received in eachline; labeling the incoming segment of the one or more segments of databy adopting a label of an overlapping segment on a prior received linewhen the overlapping segment does not overlap any other segment of data;labeling the incoming segment of the one or more segments of data byadopting a label of an overlapping segment on a prior received line whenthe overlapping segment overlaps more than one segment on the incomingsegment when the segment is a first segment in the line buffer; andlabeling the incoming segment of the one or more segments of data byadopting a label of a last overlapping segment when more than onesegment overlaps the incoming segment.

In one aspect, the method also includes identifying one or more spatialdetails in the data according to a connectedness identified by thelabeling the one or more segments.

In another aspect, the one or more segments are received fromraster-organized data arrays wherein the data is image data arranged todisplay an image. The raster-organized data arrays can also transfer thedata as one or more of text data, numerical data, medical image data,cryptographic de-ciphering data, compressed data, and/or statisticaldata.

In one embodiment, each of the one or more segments is an unbrokensequence of one or more of a data value organized horizontally on araster line. In another embodiment, the data is one or more ofrun-length encoded data and/or data encoded by a user-directedapplication.

In another aspect, a computer program product includes a computerreadable medium configured to perform one or more acts for performinglabeling of one or more labels to one or more segments of data receivedin an incoming segment to a line buffer for propagated componentlabeling the one or more acts including one or more instructions forpreventing repeated labels in each line of the line buffer by assigninga different label for each of the one or more segments of data receivedin each line; one or more instructions for labeling the incoming segmentof the one or more segments of data by adopting a label of anoverlapping segment on a prior received line when the overlappingsegment does not overlap any other segment of data; one or moreinstructions for labeling the incoming segment of the one or moresegments of data by adopting a label of an overlapping segment on aprior received line when the overlapping segment overlaps more than onesegment on the incoming segment when the segment is a first segment inthe line buffer; and one or more instructions for labeling the incomingsegment of the one or more segments of data by adopting a label of alast overlapping segment when more than one segment overlaps theincoming segment. In addition to the foregoing, other computer programproduct aspects are described in the claims, drawings, and text forminga part of the present application.

Other embodiments disclosed include methods for propagated last labelingincluding receiving one or more data files holding segmented data;setting a maximum number of available labels as a function of a numberof label locations on a current line of memory; identifying one or moresegments in the segmented data, the one or more segments sharing a sameregion; and labeling the one or more segments as a feature of the sameregion, each feature representing a predetermined property of the sameregion. In one embodiment, the receiving one or more data files holdingsegmented data includes receiving the one or more data files asrun-length encoded data and/or data encoded by a user-directedapplication. Further, in an embodiment, the setting a maximum number ofavailable labels as a function of a number of label locations on acurrent line of memory includes determining the number of labellocations on the current line of memory in accordance with auser-directed application and/or in accordance with a predeterminedfeature in the data file.

In another embodiment, the setting a maximum number of available labelsas a function of a number of label locations on a current line of memoryincludes determining the number of label locations on the current lineof memory so that the maximum number of available labels is equal to arequired number features.

In another embodiment, the identifying one or more segments between atleast two segments in the segmented data, the one or more segmentssharing a same region includes identifying the one or more segmentsaccording to one or more region properties, the one or more regionproperties including a color and/or a texture.

In another embodiment, labeling the one or more segments as a feature ofthe same region, each feature representing a predetermined property ofthe same region includes: labeling the one or more segments as a featureusing a label from a dedicated label queue, the maximum number of labelsin use being equal to a number of possible label locations on thecurrent memory line.

In another embodiment, labeling includes determining a maximum number ofunique segments on the current memory line; setting a number of possiblelabel locations as the maximum number; for each segment that does notconnect to a segment on the line below, closing that segment to enablereuse of an associated feature label and feature memory location; foreach segment that connects to a segment on a line below, and the segmenton the line below only connects to one segment, propagating the labelresulting in no net alteration in a number in use labels; and for eachsegment that connects to more than one segment on a line below,propagating a label for connected segments in the current line ofmemory.

In another aspect, a computer program product includes a computerreadable medium configured to perform one or more acts for performing toperform one or more acts for performing propagated last labeling, theone or more acts including one or more instructions for receiving one ormore data files holding segmented data; one or more instructions forsetting a maximum number of available labels as a function of a numberof label locations on a current line of memory; one or more instructionsfor identifying one or more segments between at least two segments inthe segmented data, the one or more spaces sharing a same region; andone or more instructions for labeling the one or more spaces as afeature of the same region, each feature representing a predeterminedproperty of the same region.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting theherein-referenced method aspects; the circuitry and/or programming canbe virtually any combination of hardware, software, and/or firmwareconfigured to effect the herein-referenced method aspects depending uponthe design choices of the system designer.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is NOT intended to be in any way limiting. Otheraspects, features, and advantages of the devices and/or processes and/orother subject described herein will become apparent in the text setforth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the subject matter of the present applicationcan be obtained when the following detailed description of the disclosedembodiments is considered in conjunction with the following drawings, inwhich:

FIG. 1 is a block diagram of an exemplary computer architecture thatsupports the claimed subject matter;

FIGS. 2A and 2B, both labeled “prior art” illustrate a label buffer anda prior art label buffer process, respectively.

FIG. 2C is a flow diagram in accordance with an embodiment of thepresent invention.

FIG. 3 is a schematic diagram illustrating an embodiment of regioninformation memory in accordance with an embodiment of the presentinvention.

FIG. 4 is a flow diagram in accordance with an embodiment of the presentinvention.

FIG. 5 is a flow diagram in accordance with an embodiment of the presentinvention.

FIG. 6 is a flow diagram in accordance with an embodiment of the presentinvention.

FIG. 7 is a schematic diagram illustrating label buffer, label list andregion list storage in accordance with an embodiment of the presentinvention.

FIG. 8 is a flow diagram in accordance with an embodiment of the presentinvention.

FIG. 9A is a schematic diagram illustrating an alternate embodiment ofthe present invention.

FIGS. 9B and 9C are flow diagrams illustrating a method in accordancewith an embodiment of the present invention.

FIG. 10A is a system diagram illustrating a system in accordance with anembodiment of the present invention.

FIG. 10B is a flow diagram illustrating a method in accordance with anembodiment of the present invention.

FIG. 11 is a flow diagram in accordance with an embodiment of thepresent invention.

FIG. 12 is a schematic diagram of a label buffer in accordance with anembodiment illustrating how regions are combined and propagated inaccordance with an embodiment of the present invention.

FIG. 13 is a flow diagram illustrating a method in accordance with anembodiment of the present invention.

FIG. 14 is a schematic diagram of a label buffer current line andprevious line in accordance with an embodiment illustrating inaccordance with an embodiment of the present invention.

FIG. 15 is a flow diagram illustrating a method in accordance with anembodiment of the present invention.

FIG. 16 is a flow diagram illustrating a method in accordance with anembodiment of the present invention.

FIG. 17A labeled “prior art” is a prior art diagram of a connectedcomponent labeling scheme illustrating how data can be combined to asingle region.

FIG. 17B is a diagram illustrating connected component labeling thatidentifies features in accordance with an embodiment of the presentinvention.

FIG. 18A illustrates data as input data written into a label buffer inaccordance with an embodiment of the present invention.

FIG. 18B is a diagram illustrating input data written to a label bufferand labeled segments in accordance with an embodiment of the presentinvention.

FIG. 18C is a diagram illustrating input data written to a label bufferas compared to input data in accordance with an embodiment of thepresent invention.

FIG. 19 is a flow diagram illustrating a method in accordance with anembodiment of the present invention.

FIG. 20 is a flow diagram illustrating a method in accordance with anembodiment of the present invention.

FIG. 21 illustrates exemplary data structures in accordance with anembodiment of the present invention.

FIG. 22 is a schematic data flow diagram illustrating a method inaccordance with an embodiment of the present invention.

FIG. 23 is a schematic data flow diagram illustrating a networkedapplication method in accordance with an embodiment of the presentinvention.

FIG. 24 is a sample precision based connected component example of amethod in accordance with an embodiment of the present invention.

FIG. 25 is a flow diagram illustrating a method in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION

Those with skill in the computing arts will recognize that the disclosedembodiments have relevance to a wide variety of applications andarchitectures in addition to those described below. In addition, thefunctionality of the subject matter of the present application can beimplemented in software, hardware, or a combination of software andhardware. The hardware portion can be implemented using specializedlogic; the software portion can be stored in a memory or recordingmedium and executed by a suitable instruction execution system such as amicroprocessor.

With reference to FIG. 1, an exemplary computing system for implementingthe embodiments and includes a general purpose computing device in theform of a computer 10. Components of the computer 10 may include, butare not limited to, a processing unit 20, a system memory 30, and asystem bus 21 that couples various system components including thesystem memory to the processing unit 20. The system bus 21 may be any ofseveral types of bus structures including a memory bus or memorycontroller, a peripheral bus, and a local bus using any of a variety ofbus architectures. By way of example, and not limitation, sucharchitectures include Industry Standard Architecture (ISA) bus, MicroChannel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus also known as Mezzanine bus.

The computer 10 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby the computer 10 and includes both volatile and nonvolatile media, andremovable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by the computer 10. Communication media typically embodiescomputer readable instructions, data structures, program modules orother data in a modulated data signal such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media. Combinations of the any of the above should also beincluded within the scope of computer readable media.

The system memory 30 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 31 andrandom access memory (RAM) 32. A basic input/output system 33 (BIOS),containing the basic routines that help to transfer information betweenelements within computer 10, such as during start-up, is typicallystored in ROM 31. RAM 32 typically contains data and/or program modulesthat are immediately accessible to and/or presently being operated on byprocessing unit 20. RAM 32 is shown with operating system 34,application programs 35, program module 36, and program data 37. By wayof example, and not limitation, FIG. 1 illustrates non-removablenon-volatile memory interface 40 connected to hard disk drive 41configured to hold operating system 44, application programs 45, andprogram module 46 and program data 47 in accordance with an embodimentas described herein.

The computer 10 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 1 illustrates a magnetic disk drive 51 that reads from or writes toa removable, nonvolatile magnetic disk 52, and an optical disk drive 55that reads from or writes to a removable, nonvolatile optical disk 56such as a CD ROM or other optical media. Other removable/non-removable,volatile/nonvolatile computer storage media that can be used in theexemplary operating environment include, but are not limited to,magnetic tape cassettes, flash memory cards, digital versatile disks,digital video tape, solid state RAM, solid state ROM, and the like. Thehard disk drive 41 is typically connected to the system bus 21 through anon-removable memory interface such as interface 40, and magnetic diskdrive 51 and optical disk drive 55 are typically connected to the systembus 21 by a removable memory interface, such as interface 50. Aninterface for purposes of this disclosure can mean a location on adevice for inserting a drive such as hard disk drive 41 in a securedfashion, or a in a more unsecured fashion, such as interface 50. Ineither case, an interface includes a location for electronicallyattaching additional parts to the computer 10.

The drives and their associated computer storage media, discussed aboveand illustrated in FIG. 1, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 10. In FIG. 1, for example, hard disk drive 30 is illustratedas including program module 36 and program data 37. Program module 46could be in non-volatile memory in some embodiments wherein such aprogram module that runs automatically in an environment. In otherembodiments, program modules could part of an embedded system.

A user may enter commands and information into the computer 10 throughinput devices such as a microphone 63, a keyboard 62 and pointing device61, commonly referred to as a mouse, trackball or touch pad. Other inputdevices (not shown) may include a joystick, game pad, satellite dish,scanner, or the like. These and other input devices are often connectedto the processing unit 20 through a user input interface 60 that iscoupled to the system bus, but may be connected by other interface andbus structures, such as a parallel port, game port or a universal serialbus (USB). A monitor 91 or other type of display device is alsoconnected to the system bus 21 via an interface, such as an outputperipheral interface 94. The monitor 91 may also be integrated with atouch-screen panel or the like. Note that the monitor and/or touchscreen panel can be physically coupled to a housing in which thecomputing device 10 is incorporated, such as in a tablet-type personalcomputer. In addition, computers such as the computing device 10 mayalso include other peripheral output devices such as speakers 97 andprinter 96, which may be connected through an output peripheralinterface 94 or the like.

The computer 10 may operate in a networked environment 108 using logicalconnections to one or more remote computers, which could be other cellphones with a processor or other computers. As shown, network 108 is awired or wireless network or internet, connecting sensors 102(1-3), auser computer 104, a user cell phone 106 or other mobile device, and/oran application input, output or other or further processing 107. Theuser computer 104 may be a personal computer, a server, a router, anetwork PC, PDA, cell phone computer, a peer device or other commonnetwork node, and typically includes many or all of the elementsdescribed above relative to the computer 10, although only a memorystorage device 81 has been illustrated in FIG. 1. The logicalconnections depicted in FIG. 1 include a local area network (LAN) 71which can be a wide area network (WAN) 78, but may also include othernetworks. Such networking environments are commonplace in offices,enterprise-wide computer networks, intranets and the Internet. Forexample, in the subject matter of the present application, the computersystem 10 may comprise the source machine from which data is beingmigrated, and the remote computer may comprise the destination machine.Note however that source and destination machines need not be connectedby a network or any other means, but instead, data may be migrated viaany media capable of being written by the source platform and read bythe destination platform or platforms.

Computer 10 can be connected to hardware configured in accordance withembodiments disclosed herein to accomplish connected component labeling.Computer 10 can be output device to collect data or can be included inthe processing required for implementing disclosures herein. Thus,computer 10 can be agnostic to one or more disclosures and/or beincorporated as a necessary part of an embodiment as will be appreciatedby one of skill in the art.

Connected Component Labeling for Segmentation

Embodiments herein relate to computer and/or processor directed actionsfor connected components labeling (CCL) Connected Component Labeling(CCL) for designating or labeling of groups or regions of related data.CCL commonly provides the labeling function for segmentation.

In general, the data being considered can include data arranged in“raster” order, typically rows of width w stacked from top to bottomwith a height of h rows. For example a specific element of image data isreferenced by location, the pixel value at x width and y height is p(x,y). Referring to FIG. 2A, a raster ordered buffer example is illustrated200 which illustrates how data input is input and how lines in thebuffer move up.

Segmentation applies relationship rules to arrays of data. The input tothe segmentation function is individual data values. The output ofsegmentation is the result of iteratively applying the relationshiprules to pairs of adjacent data values or groups until the whole arrayis processed. For example, if the rule is “segment pixels of the samecolor,” then the segmentation function will indicate any groups orregions of matching pixels. Likewise, embodiments can be directed tosegmentation based on shape or other characteristics.

Prior art techniques for connected component labeling begin with datalocations that are “Labeled” or marked with unique designations. A labelbuffer can match the data buffer in size, so that each data location hasa corresponding label location.

Prior art labeling provides that labels are associated in a linked list(label list) so that linked labels indicate related data. All of thedifferent labels for different areas of a region point to one label, the“root” or “region” label. A common convention is to choose the lowestvalued label. For example the related data elements labeled 73 and 74will be entered into the label list as 74→73, and 73→73, or 74 points to73 and 73 points to itself. The region label is therefore 73. FIG. 2B“prior art” illustrates how labels are combined to provide label 73 202A function called “Union-Find” containing the function “Find-Root” iscommonly used to perform the root search and connection of label pairs.The data values for locations 73 and 74 are combined so that the label73 represents the collection of data at locations corresponding tolabels 73 and 74.

A prior art example CCL method allows region labels to be the uniquedesignators for data structures in memory that hold region informationuseful to segmentation. For example, for segmenting pixels by color, theregion information structure addressed by a region label would containthe average color of that region. After segmentation, important regioninformation has been collected, and the data can be handled as regions(#73, #68) instead of individual data elements.

In summary, CCL and segmentation are interrelated functions. CCL labelsand groups data locations selected by segmentation rules. Segmentationanalyzes individual data values and the collected region qualities fromCCL.

The disadvantages of prior art CCL technique are numerous. For example,if the amount of data is n elements, n locations in memory for labelsand region information must be allocated. Also, for prior art methods, awhole array of data must be labeled and then connected, requiringmultiple passes and several operations for each data element. In priorart techniques, region growth is unpredictable, making collection ofdetails about region shape expensive and difficult because anothercomplicated and costly pass over the whole data set is required. As aresult of prior art methods of performing CCL, the results obtained arepresented ad hoc. In other words, there is no standard format forencoding and communicating region features.

Referring now to FIG. 2C, a flow diagram illustrates a method accordingto an embodiment. As shown, block 210 provides for “region” labeling ofgroups of raw data, including designating data structures containinginformation about whole regions. Block 220 provides for re-labelingregions into subregions as a “feature” labeling to expose spatialdistribution of region features.

Block 220 includes optional block 2202 which provides for collecting andcommunication information about subregions using a feature datastructure. More particularly, feature labels can designate the featuredata structures, such as a feature data structure for containinginformation about subregions. The feature data structure can provide forcollecting and communicating feature information. As a result, featurescan be acquired with controllable precision that uses an architecturethat minimizes the memory required for pre-CCL actions, such assegmentation. By designating feature labels, memory required foracquiring region features is optimized.

In one embodiment, as illustrated in optional block 2204, the methodprovides for recording shapes and sizes of regions on previouslysegmented data. In another embodiment, the method provides in optionalblock 2206 for performing CCL to raw data by incorporating asegmentation rule for determining which data elements are related andshould be “connected.” Thus, CCL can operate on labels applied duringsegmentation or CCL can be configured to re-label data without regardfor previous processing.

In general the data being considered is arranged in “raster” order,typically rows (lines) of width w stacked from top to bottom with aheight of h rows. For example a specific element of image data isreferenced by location, the pixel value at x width and y height is p(x,y).

Block 230 provides for setting up a memory array with a 1:1correspondence to a data array. More particularly, the data in the dataarray is labeled by setting up a memory array that is addressable in a1:1 correspondence to the data array. For example, if the data isindexed by p(x, y), then the labels can be indexed by Lp(x, y). Depictedwithin block 230 is optional block 2302 which provides for using memorywith a linear address space. Thus, labels can be indexed by p(m), wherem is equivalent to (x, y), for example m=(y*w)+x. Additional or optionalappropriate spatial conversion and/or memory address offset calculationsmay be required. For example, the memory address and contents will beLabel[73]=73 and Label[74]=73 for the example. Regions 73 and 74 aremerged. Referring to FIG. 3, an exemplary CCL operation on label memoryis illustrated. As shown, raw data 310 is identified as regions 73 and74 as labels in label memory 320. Next, labels are connected by pointers330 and finally, a region label 340 is produced as label 73.

Additionally, some region qualities might be collected such as regionsize, shape, or color. Region qualities can be combined such that nomore memory is required to store the result of combining regions. Forexample, “average region color” will require the same amount of storagespace regardless of how many regions were combined during segmentation.The region qualities are stored in memory addressed by region labelswith address conversions and/or offsets as needed.

The CCL operations on region information memory are illustrated in FIG.4. Specifically, as shown, there can be four regions. Raw data 410 isidentified as region metrics in region information memory 420, theregion information in 74 is then combined with 73 in block 430, andregion information for region 73 is then stored 440. Each of the memoryareas have the same indexing scheme taken from the original data labelsuch that the original data label provides the raw data index, datalabel index, region label index and region information index. The sameindexing scheme can provide different kinds labels for data, region,border information and other types of information about the raw data410.

Referring now to FIG. 5, an embodiment is directed to a method forstoring region data. Block 510 provides for assigning region labelsindependent of data labels. Rather, block 520 provides for assigningregion labels in an order determined as a function of label demand.Block 530 provides that a region information index is assignedindependent of region labels. Rather, block 540 provides for assigning aregion information index in an order determined by region informationindex demand. As a result of the independence from data labels andregion labels, it has been discovered that line buffer segmentationprocesses are supported and Union-Find and Find-Root algorithms aresupported while minimizing memory usage. Moreover, assigning an orderbased on index demand can be applied to different types of data. Forexample data labeling beyond data and region labeling can include borderdata, color data, subregion data, and stochastic data.

Line Buffer

A line buffer, for purposes of the present disclosure, can include oneor more lines of memory, each line of memory containing a line of data,which can be image data. The data may be stored in different formats,such as a run length encoded (RLE) format. The data locations making upthe lines of memory and the lines of memory making up the line buffermay or may not be contiguous memory locations.

Referring now to FIG. 6, a method for operating a CCL using a linebuffer is illustrated. More particularly, a data stream from a videocamera or computer network can be processed in a data buffer (memory) bywriting the newest line of data at the bottom of the buffer while movingall of the existing data in the buffer up one row. As a result,segmentation methods are supported that can operate on data in linebuffers. For example, a segmentation process can be executed such thatthe upper lines of the buffer are completely segmented before the lowerlines of the buffer.

Block 610 provides for moving all of the data in a line buffer up onerow when a top row of data is fully segmented. Block 620 provides forentering the next data line in the bottom row of the buffer. Block 630provides for segmenting the data in the buffer. Block 640 provides formoving all the data in the buffer up one row when the top row of data isfully segmented. Block 650 provides for entering the next data line inthe bottom row of the buffer. Block 660 provides for repeating theprocess until complete.

Referring back to FIG. 2A, the line buffer is illustrated can that isappropriate for embodiments. More particularly, FIG. 2A includes severallines and illustrates apparent movement of the line buffer over theinput data, such as an image. Advantageously, instead of buffering thefull image, only a few lines at a time are stored in memory. Further,the actual xy coordinates of data can be derived from the xy coordinatesin the buffer along with the location of the buffer over the completedata array. Next, region labels are assigned to the data bufferlocations.

Referring now to FIG. 7, labeled “prior art”, a label buffer 710 isillustrated on the left, followed by a label list 720 and a region list730 is illustrated on the right. The labels in a region buffer point tothe region list 730 which also points to the list of region structureslocated in memory.

Arbitrary Labeling

According to an embodiment, label index allocation can be arbitrarilyassigned such that labels have a random quality. For example, a labelallocation can be assigned 1, 8, 2, 25, etc., such that labels areneither in increasing order or in an ordered sequence. Further, labelingcan be performed such that the relationship between data labels, regionlabels, and region information indices is made more independent whilemaintaining label coherency, thereby enabling arbitrary and independentlabeling. Referring to FIG. 8, a flow diagram illustrates a method forenabling arbitrary and independent labeling for CCL. Block 810 providesfor choosing a label for a region containing the “earliest data” as aroot when connecting related data elements. For example, for theraster-ordered example, the top row is y=0 and the first column is x=0.A segmentation process might expect labels ordered as shown on the toptwo rows. The “earliest data” will have the lowest result for ((y*w)+x).The data labeled 91 and 75 are linked. Label 91 is selected as rootaccording to the test. According to an embodiment, other conventions forselecting the root label can be adopted.

Block 820 provides for flagging or otherwise marking the root label.Specifically, the root label no longer points to itself, so the rootlabel is flagged or marked. For example, an unused memory bit may beset.

Referring to FIG. 9A in combination with FIG. 7, label buffer 910includes an arbitrary label “91” that is arbitrary but indexed by labellist 920 to location r23. Region list 930 identifies region R[23] asincluding r23 and r7 information. According to an embodiment, theflagged root label contains the region list index. In the example, the“r” in the label list 920 at L[91] indicates a region list index. L[75]has had its original r7 index overwritten by a pointer to L[91].

Label Reuse and Memory Allocation Efficiency

Referring now to FIG. 9B, a method for label reuse is shown in a flowdiagram. Block 940 provides for assigning one or more labels to one ormore groups of raw data representing one or more regions by designatingone or more data structures as containing information about the one ormore regions. For example, label buffer 910 in FIG. 9A can interact withinstructions received from a module, ASIC or the like. Block 950provides for connecting the one or more labels determined to be related.Block 960 provides for choosing a root label for the connected one ormore labels, the root label determined by locating an earliest dataelement from the one or more groups of raw data. In one embodimentchoosing a root label for the connected one or more labels includesdetermining the earliest data element according to a lowest result for((y*w)+x) wherein y represents a row value, x represents a column value,and w represents a width value. The earliest data element can beaccording to a first-in-first-out (FIFO) algorithm for raster-organizeddata arrays. The choosing can also be accomplished by flagging the rootlabel by setting an unused memory bit when a prior root label no longerpoints to itself.

Block 970 provides for altering a label list of the one or more labels,the label list altered by flagging the root label to include a regionlabel index. For example, the altering can include flagging the rootlabel by setting an unused memory bit when a prior root label no longerpoints to itself. Block 980 provides for overwriting one or more regionlabel indexes according to the root label. Thus, region label indexesare reused. For example, a pointer can be provided to the root label.

Referring now to FIG. 9C a flow diagram illustrates a method for reusingone or more labels in a connected component labeling system. Block 990provides for determining a location value for each of the one or morelabels, each location value identifying a maximum “y” extent (“yMax”) ofan associated label region. For example, determining a location valuecan include assigning as the yMax value a y coordinate for each label ofthe one or more labels located in a memory, the y coordinate assignmentbased on a row level in the memory. In one embodiment, determining the ycoordinate can be a function of the row level in the memory wherein thememory is a data buffer configured to receive label and regioninformation in a receiving row. In an embodiment, determining the rowlevel can be according to raster order of rows wherein the rows of areformed by moving data up from a bottom row to a top row. For example,label and region information could include receiving in the memory anddetermining the y coordinate as a function of the row level in thememory wherein the memory is a data buffer configured to receive labeland region information in a bottom row.

Block 992 provides for determining which of the one or more labels referto areas subsumed in a determination of the yMax location value. Whendetermining which of the labels refer to areas subsumed, an embodimentdirects storing a location of each root in an associated regioninformation location to enable label comparison during subsuming oflabel data.

Block 994 provides for reusing the one or more labels and/or regioninformation memory location values subsumed in the determination of theyMax location value.

In one embodiment, the method for reusing includes determining that arow of the memory is fully processed and/or about to be overwritten.Next, for each label in the fully processed and/or about to beoverwritten row, the method directs comparing each yMax value to a ycoordinate for the row. For each yMax value that matches the ycoordinate, the method calls for designating an associated label as anavailable label. For each yMax value that does not match the ycoordinate, the method then directs determining that the yMax value isassociated with a series of labels. Next, the method compares each yMaxvalue in the series of labels to the y coordinate. For each yMax valuein the series of labels that matches the y coordinate, the methoddesignates an associated label as an available label.

In another embodiment, the reusing includes determining whether the oneor more labels and/or region information memory location values are partof a tree structure and locating a root of the tree structure to enablereusing an associated label and/or region information memory locationvalue associated with the root.

In another embodiment, the reusing includes enabling each yMax locationvalue to propagate from root value to root value of each region suchthat each region root contains a yMax location value that is an actualmaximum y coordinate.

Block 996 provides for comparing two or more yMax values to determine amaximum yMax value. Block 998 provides for assigning the determined yMaxvalue as a root result.

Referring now to FIG. 10A, a connected components labeling systemappropriate for enabling label reuse is illustrated. FIG. 10A shows aprocessor 1002, and a memory 1003 coupled to the processor 1002. Memory1003 includes a circular buffer 1004 configured to contain a pluralityof locations for holding one or more region labels for the connectedcomponents labeling system and a label buffer 1005 coupled to thecircular buffer. Label buffer 1005 is configured to be initialized witha sequential count at the beginning of an operation for each array ofinput data in a raster order. Further, label buffer 1005 holds currentlabels in use during an operation, and is independent from the height ofa connected region. FIG. 10A also illustrates a label list 1006 coupledto the label buffer 1005 and the circular buffer 1004 the label list1006 receives closed labels no longer required by label buffer 1005.Label list 1006 can be initialized with a maximum number of labelposition to prevent a last label from overtaking a lead label and returnlabel availability to circular buffer 1004 in ascending order.

FIG. 10A further includes region queue 1007 coupled to label list 1006.Region queue 1007 can be configured to send and receive a plurality ofregion information locations equal to a number of possible roots. FIG.10A further can include region information module 1008 coupled to labellist 1006. In an embodiment, region information module 1008 outputsregion information wherein the maximum number of region informationlocations is equal to the number of possible roots, the number of regioninformation locations being a fraction of the maximum number of regioninformation locations.

Referring now to FIG. 10B, another embodiment is directed to enablingreuse of label and region information in memory locations that are nolonger needed. More specifically, one problem is that labels that arenot roots and no longer in use inside the buffer area may be required bya Find-Root function. Block 1010 provides for creating label chains bysuccessive merge operations on roots with decreasing ((y*w)+x) values.For example, a chain of labels such as 5-65-74-342-2-57(root) might becreated. If any of these labels is destroyed, a Find-Root on 5 will notreturn 57. Even if 65, 74, 342, 2 and 57 have moved out of the bufferand are no longer being processed by segmentation, an attempt to performsegmentation with label 5 will fail. The problem is that there is no wayto detect when a label is no longer needed without scanning the wholelabel list.

Block 1020 provides for attaching a location value to every label torecord the maximum y extent (“yMax”) of the label's region. The yextents are the top and bottom locations of a region's spatial location.In one embodiment, the method provides that only the root result of eachUnion-Find operation has the yMax value updated. Even if some methodother than Union-Find is used there is still an implicit comparison oftwo roots from which one root will remain. Only two operations arerequired, compare and write, both of which can be executed in parallelwith other Union-Find operations so that no time or delay is added tothe CCL process.

Block 1030 provides for assigning as the label for the bottom row of thelabel buffer yMax as the y coordinate for the buffer bottom row.

Block 1040 provides for comparing the yMax for each root when two rootsare compared during Union-Find or the equivalent and selecting thelarger valued yMax. Block 1050 provides for updating the root result ofthe Union Find or equivalent with the new yMax.

Block 1060 provides for checking each yMax of each label in the top rowof the label buffer when top row of label buffer is completely processedand ready to be reused (or labels in the label buffer's top row areabout to be overwritten) then for each label in the top row of the labelbuffer. More specifically, block 10602, depicted within block 1060provides that if a label's yMax=the y coordinate for the buffer top row,the label can be reused (return label to queue), which optimizes memoryusage. Block 10604, also depicted within block 1060 provides that if thelabel is not root, then it is part of a chain of labels. Block 106042provides for checking yMax for the next label in the chain. Block 106044provides for reusing the label by returning the label to the queue ifyMax=the y coordinate for the buffer top row and continuing to considerthe chain of labels. Chains in accordance with an embodiment can becreated by successive merge operations. Successive merge operationspropagate higher yMax from root to root. As the chain is traversed toroot, yMax can not decrease. Label chains and label reuse in connectedcomponent labeling enables processing for any sized object. Morespecifically, the yMax procedure explained above is agnostic to at leastone dimension of data, which enables segmentation and CCL in a linebuffer. Moreover, by successive application of the yMax procedure, thenumber of dimensions of data can be extended as needed by systemrequirements.

As a result of the method described in FIG. 10B, the CCL is based onmerging regions by pointing one region root at another region rootresulting in one root, thereby making every region have one root.Further, only a region's root yMax is updated. Further, every label iseither root or has been merged as root at least once during the process.Further, only roots are merged. After operating the method, the yMaxpropagates from root to root. Therefore, for every region, the regionroot contains the yMax that is the actual maximum y coordinate for thatregion. The non-root labels in the region are part of a chain ofpointers to root. No labels will escape reuse, even if several chainscombine before the root is reached (tree structure). Therefore, themethod reuses labels completely, and no labels are missed.

In one embodiment, when a label and label memory is initially allocatedfor use, for example, when a label is applied at the bottom of thebuffer, or when a region info structure is filled with a new label'sdata, the memory space is termed “opened.” When the memory space is nolonger needed and can be reused it is “closed.”

As a result of reusing labels and the methods as described above, theregion information memory locations that were potentially unused afterregion merge operations are now known to be available for reuse, whichoptimizes memory. For every merge operation between two regions, oneregion label will no longer be root. Every root label has acorresponding region information structure in memory.

From the time a label is no longer root to the time the label is closedby the yMax technique, the region information structure for that labelis unused. In an embodiment, yMax is stored in the label list instead ofthe region list. Because yMax is stored in the label list, the non-rootregion info structure has no useful information. As a result, the regioninfo structure can be closed as soon as the region label pointing to itis no longer root. Therefore, for every region merge operation, oneregion info structure can be closed. The region info locations pointedto by the root labels can be closed when the corresponding root label isclosed according to yMax.

Note that if linking conventions need to be maintained, then the actual(x, y) location of each root can be included in the region infolocations. The actual (x, y) is useful for comparing labels to chooseroot during merge operations.

As a result of the method described both region labels and regioninformation structures now have exact conditions when they can beclosed. Memory usage is made maximally efficient because memorylocations are closed as soon as the memory location is no longer needed.Region labels can be closed when yMax is satisfied. When a root label isclosed by the yMax method, the associated region information structurecan be closed. When a root label becomes a non-root label during regionmerging, the non-root region information memory space can be closed. Inone embodiment, the method using yMax is also key to making the linebuffer technique work for CCL. Thus, any size region can be segmented inany size line buffer, which is a tremendous savings in memory and abreakthrough for CCL in embedded applications.

Label Queue

A problem with label queuing includes the closing of region labels andregion information locations in an unpredictable order. CCL works witharbitrary labels if the methods disclosed herein are applied. Thelabeling methods herein described enable reallocation and opening oflabels and region information locations in the order in which they wereclosed.

The number of labels required for the CCL methods disclosed hereinefficiently result in less than the width multiplied by height amount oflabels required with known techniques. The number of labels required asherein disclosed is a function of both buffer size and a chosensegmentation method. Specifically, the number of labels increases withthe size of the buffer; the number of labels decreases with the increasein aggressiveness and/or effectiveness of a chosen segmentation method.Label requirements also vary with differing image or data conditions.Fore example, an image with many objects can require many labels.Therefore, according to an embodiment, a method allows for optimizationof memory usage according to one or more of image conditions,application requirements, and/or segmentation techniques. Morespecifically, in an embodiment, image conditions can be such that memorymust be allocated for fine details and an embodiment addresses the labelrequirement for such known conditions. For example, an image of adetailed tree could require memory allocation for leaf regions. Incontrast, a memory allocation that prioritizes application usage couldbe configured to ignore leaf detail requirements in memory. Likewise, amemory allocation that prioritizes segmentation techniques could ignoreleaf details as well.

Referring now to FIG. 11, a flow diagram illustrates a method inaccordance with an embodiment. Describing the required number of labelsas maxLabels, the required number of region information locations issmaller than maxLabels. Allow the data name maxInfo to represent thenumber of region information locations. Block 1110 provides for creatinga circular buffer, or “queue,” with maxLabels locations holding all ofthe region labels. Block 1120 provides for creating an array“LabelQueue[maxLabels]” as a circular list with a head and tail index.Block 1130 provides for opening a next label as openlabel=label[head].Block 1140 provides for returning a closed label to the list atlabel[tail] closedlabel. Block 1150 provides for initializing the labellist with label[i]=i, i=0 . . . maxLabels.

Initially, head=0 and tail=0. Thus, labels are first opened in ascendingorder, 0—maxLabels. Labels return to the circular list for reuse in theorder closed. The label list will become disordered but will remaincorrect (tail will not overtake head), because there can only be amaxLabels number of labels open at a time. The RegionQueue[ ] alsofollows the same method described in FIG. 11.

In an alternate embodiment, block 1160 provides for reducing the labelqueue size by initializing the label buffer with a sequential count atthe beginning of a segmentation or CCL for each full array of inputdata. For example, when a new image is to be segmented or labeled viaCCL, the method directs initializing the label buffer from a counterwith a sequential count applied to each buffer location in raster order.As a result, the label queue size required is large enough to containthe number of labels that might be required to label chains and rootsfor the portion of regions that extend outside the top of the labelbuffer.

The absolute maximum number of region info locations is equal to thenumber of possible roots, i.e., the number of locations in the linebuffer plus one row. The actual number of region information locationsresults in some fraction of the maximum unless a segmentation erroroccurs.

In accordance with embodiments disclosed herein, the maximum number oflabels becomes greater than the maximum number of roots due to labelchains that cross the top row of the buffer. More specifically, themaximum number of labels results in the number of buffer locations plusan estimate for the number of links outside the buffer on chains thatcross the top row of the buffer, plus the number of roots above the toprow of the buffer for regions that cross the top row of the buffer.

Referring now to FIG. 12, segmentation performed on a line buffer 1200is illustrated showing the flow of data through the buffer. The raw datainput 1210 is grouped with similar neighbors until larger regions 1220are created. As the data moves up from the start of segmentation 1230through the buffer until segmentation ends 1240, the regions in buffer1200 become larger.

At the top of the line buffer 1200, segmentation is practically finishedand the 1:1 relationship between labels and raw data locations is at anend. The result of segmentation needs to be acquired from the top lineat data output 1250. Segmentation precedes feature labeling, or the datais in a form that is effectively segmented. For example, text made ofblack characters on a white background. For ease of discussion, theinput to feature labeling will be shown as line segments that areconsidered to be already labeled; the regions are known. The use of linesegments does not preclude the use of Feature Labeling on unsegmented,unlabeled, or raw data.

Line Segments from Segmentation Labels

Referring now to FIG. 13, a diagram illustrates a method for a simplemodel made of line segments. Labeled line segments represent the inputdata. Line segments with labels pointing to the same root are consideredto be connected. Block 1310 provides for relabeling segmented data.Block 1320 provides for representing data that is known to be connectedbecause of some discernable quality or because it has been labeled andthe roots are known by overlapping line segments. Block 1330 providesfor operating on line segments from two lines. Referring to FIG. 14, aFeatures Label diagram 1400 illustrates the simple model with the“Current Line” 1410 representing the newest data or the segmented datafrom the top of the segmentation label buffer. The “Previous Line” 1420represents the previous line of input data or the previous line takenfrom the top of the segmentation label buffer. Empty spaces betweensegments represent data that has no connections relevant to the currentdiscussion.

Line Segments from Data

erring to FIG. 15, a raster-organized data 1500 is arranged inhorizontal lines read left to right. The lines are stacked top to bottomto make a 2-D array such as image data arranged to display an image. Forexemplary purposes, this disclosure represents a segment, such as a linesegment, as an unbroken sequence of one or more of the same data valueorganized horizontally on a raster line. Other ways of organizing orrepresenting data can be considered a segment. Data can be Run-LengthEncoded (RLE). Other data relationships may be chosen by a user or otherrelationship choosing automatic or responsive program as required by theapplication. For example, segments can be created by dissimilar data ordata with arbitrary relationships. Non-linear relationships can beapplied to the data, such as connecting data based on spatialrelationships such as shape.

A common Union-Find data structure is known as the linked-list. Labelsare the indexes of the linked list locations and the data of eachlocation is a pointer to another label. The labels are linked by thepointers to associate the labels representing a group of relatedsegments. One of the labels in a linked group is chosen as the regionnumber or “root”. Typically, the root is the lowest label number. Forexample, labels 1, 2, 3, 4, 5, 6, and 7 can point to label 1 as theregion number (root). The pointing creates a tree structure wherein theelements (labels 1, 2, 3, 4, 5, 6, 7) point to the root (label 1).

The input data elements are numbered (labeled) and each label is anindex of the linked list. For example, data element #3 is listed asLabel[3]=3. As the connected data labels are linked, all of the labelswill point to the root or region label.

Modified Propagate Last Labeling

According to an embodiment, a method for modified propagated lastlabeling is provided that enables collection of region features (featureencoding) using only one line of data memory and the equivalent of oneline of feature structures. According to an embodiment, the input toPropagate Last Labeling includes using known-connected line segmentswhich are to be relabeled with regard to connectedness but withoutregard to previous labeling except to take advantage of labelsindicating connectedness. In one embodiment, a labeling method numbersand/or labels each of the segments in raster order. In the embodiment,the root label designates the region segments.

Propagated labeling saves memory space by immediately reusing labels.Labels are applied as the data arrives, left to right, top to bottom.Overlapping segments take the label from the segment above.

“Modified Propagate Last” refers to an embodiment that abides by alabeling rubric in which any label occurs at most once per line;segments below take the label of the overlapping segment above; if onesegment above overlaps more than one segment below, the first segmentbelow gets the label from the segment above; if more than one segmentabove overlaps one segment below, the segment below takes the label fromthe last overlapping segment above.

The rubric enables exposing all spatial details of the region; also,labels designate unique areas of a region containing spatial details.Moreover, the rubric enables a very efficient hardware process. All ofthe necessary segment and label states are known at the end of eachsegment on the current line for labeling that segment. Therefore FeatureLabeling can proceed as data arrives with one line of memory forbuffering the label process.

Referring now to FIG. 16, the modified propagated last labeling methodis illustrated that enables exposing of all spatial details of a region.Specifically, block 1610 provides for preventing repeated labels in eachline of the line buffer by assigning a different label for each of theone or more segments of data received in each line buffer. Block 1620provides for labeling the incoming segment of the one or more segmentsof data by adopting a label of an overlapping segment on a priorreceived line when the overlapping segment does not overlap any othersegment of data. Block 1630 provides for labeling the incoming segmentof the one or more segments of data by adopting a label of anoverlapping segment on a prior received line when the overlappingsegment overlaps more than one segment on the incoming when the segmentis a first segment in the line buffer. Block 1640 provides for labelingthe incoming segment of the one or more segments of data by adopting alabel of a last overlapping segment when more than one segment overlapsthe incoming segment. Block 1650 provides for identifying one or morespatial details in the data according to the connectedness identified bylabeling the one or more segments.

The segments can be received from raster-organized data arrays whereinthe data is image data arranged to display an image, and/or can be froman unbroken sequence of a data value organized horizontally on a rasterline, and/or can be run-length encoded data and/or data encoded by auser-directed application.

For clarity, the convention will be to refer to “unique areas of aregion containing spatial details” as “features.” Labels thereforedesignate features.

FIGS. 17A and 17B illustrate how modified propagate last labelingaccording to an embodiment reveals feature locations via an illustrationof extents of labels thereby illustrating how all of the features ofregion 1 are revealed. Specifically, FIG. 17A illustrated a prior artlabeling that directs labels 1710 to Region 1 1720 via a prior artprocess that results in one region for the entire data array. Incontrast, FIG. 17B illustrates a label buffer 173 that results in regionidentification 1740 with several features being made visible.

Referring now to FIG. 18, a flow diagram illustrates how to process twolines of labels with a one line buffer. The line buffer data type willbe suitable to the data being stored. Although the data may be stored inan RLE compressed format, for clarity purposes FIG. 18A illustrates dataas input data that is labeled segments 1810 and input data as writteninto a label buffer 1820. For example, if the highest valued label canbe recorded in one byte, then the line buffers could be bytes of memorywith each byte representing a buffer location that corresponds to thespatial representation of a segment.

Various methods for representing and recording labeled segments to thebuffer are within the scope of the present disclosure. For example, ifthe label for the segment is written in the last space of the buffercovered by the segment. In the figures, data in the buffer, “x” in theinput data 1820 corresponds with data for which exact data does notmatter as long as it is not a label that connects to neighboringsegments.

In accordance with an embodiment, the method of using propagate lastlabeling enables evaluation of segments as they arrive from the inputdata stream. Each segment is evaluated at the end of each segment 1830as shown in FIG. 18B.

By the end of an input segment, all of the overlapping segments for thatsegment are known. Therefore, the label for the segment in the lineabove stored in the Label Buffer can be overwritten by the new label. InFIG. 18C, after a segment below is evaluated and labeled, the segmentbelow can overwrite the segment above, as shown by arrows 1850.

As a result of the method, no valuable data in the buffer isoverwritten. If the segment in the label buffer representing a segmentin the previous line does not extend to the right of the currentsegment, then the segment above can not overlap with future segments. Ifthe segment in the label buffer representing a segment in the previousline does extend to the right of the current segment, then the portionof the segment above that might overlap future segments is notoverwritten.

Reducing Memory Requirements: Closing Features

For purposes of the present disclosure, a label designates a Featurestructure that holds the region description (features). As long as asegment on the previous line connects to a segment on the current line,that Feature might still be updated with new information, so that labelcan be referred to as “open.” According to an embodiment, the maximumpossible number of open labels is the same as the maximum number oflabels on a line. Correspondingly, the maximum number of labels on aline is the number of pixels on a line. If the segmentation process setsa minimum horizontal segment size, then the maximum number of labels ona line is (line width/min segment size). For example, the minimumsegment size for a segmented image might be one pixel. Referring to FIG.19, flow diagram illustrates a method that proves that only one labelwill occur on a line of a line buffer, whether operations are performeda line at a time or multiple lines at a time. The maximum number oflabels on a line matches the number of pixels on the line. Specifically,FIG. 19 illustrates block 1910 which provides for designating a segment.Next, block 1920 provides for labeling the incoming segment of the oneor more segments of data by adopting a label of an overlapping segmenton a prior received line when the overlapping segment does not overlapany other segment of data. Block 1930 provides for labeling the incomingsegment of the one or more segments of data by adopting a label of anoverlapping segment on a prior received line when the overlappingsegment overlaps more than one segment on the incoming when the segmentis a first segment in the line. Block 1940 provides for labeling theincoming segment of the one or more segments of data by adopting a labelof a last overlapping segment when more than one segment overlaps theincoming segment. Finally, block 1950 provides for identifying one ormore spatial details in the data according to the connectednessidentified by labeling the one or more segments.

As noted, the modified propagated labeling method in accordance with anembodiment allows any particular label to occur at most once on a line.It has been discovered that if the segment on the previous line does notconnect to a segment on the current line, it will never connect toanother segment for the rest of the image. In accordance with anembodiment, the method takes advantage of the label limit. Thatsegment's label will not be used again and the Feature it points to willnot have any more segments added. A result that is counterintuitive isthat if a segment on the previous line does not connect to a segment onthe current line, the segment label and the Feature it points to can be“closed.” There is no reason to maintain the storage of a closed Featureas that Feature will no longer be updated. The closed Feature can beoutput to whatever receiving process follows the CCL function. Forexample, the contents of the closed Feature can be written to aFirst-In-First-Out (FIFO) type memory. Thus, the closed feature can beimmediately output to the receiving process, which enables reuse of thefeature memory space to conserve memory.

Feature Encoding

According to an embodiment, an efficient method for collecting featureinformation is provided that uses a compact format for storing andcommunicating feature information. Specifically, feature encoding hereincan include collecting feature information from the raw data, or fromthe labeled data, or from the region information, and representing thatinformation in a format for collected feature information.

Using raw data enables networked sensors, e.g. cameras, that contain theprocessing required for communicating with other devices and computerson a network to efficiently pass information. The image processingrequired for scene understanding in networked cameras is limited by 1)the complexity of existing segmentation methods, and 2) the lack of amethod for encoding region information, 3) the fact that each cameraonly has one viewpoint. Each camera has an isolated viewpoint andlimited processing power. Without the feature encoding method disclosedherein, camera networks are forced to waste processing resources oncompressing video images, and to burden the network with high-bandwidthpixel representations of camera views. It is currently expensive anddifficult to track moving objects as they pass through different cameraviews. The feature encoding method herein is an efficient method forCCL/segmentation for each networked camera, a low-bandwidth format forcommunicating region information, and a way to combine the viewpoints ofcameras on the network. Feature encoding makes low-cost distributedscene understanding possible.

The actual region property, such as the color red, can be encodeddigitally as a number, for example if 42 denotes red, then (red(xmin,ymin, xmax, ymax)) may be represented in digital form as (42, 18, 735,423, 1024).

The disclosed feature encoding encompasses the extraction andcommunication of more complex characteristics. For example a series ofpoints marking the boundary of a region can be collected from theendpoints of the line segments, or the region boundary may be describedby the coefficients and constants of splines or higher-order curves.

[000136] The encoding method herein encompasses various representationsof region properties. For example a region texture may be representeddigitally as wavelet constants and coefficients. Further, disparateregion segmentations can be described. For example, a red colored regionhas an area that is smoothly textured and an area that is roughlytextured (strong edges). An adjoining blue colored region has an areathat is smooth and an area that is rough. The smooth areas arecontiguous and the rough areas are contiguous. The encoded features mayinclude the red region (blue(x1, y1, x2, y2)), the blue region (blue(x3,y3, x4, y4)), the smooth region (blue(x5, y5, x6, y6)), the rough region(blue(x7, y7, x8, y8)), or combinations such as (rough/blue(x9, y9, x10,y10)), etc.

Feature encoding enables the communication of region information 1)between different parts of a system, 2) between networked computers, 3)between networked computers and networked sensors, 4) between networkedsensors. For example one machine vision camera on a network maycommunicate to another camera on a network “do you see the red ball atlocation (x1, y1)?” The response from the second camera to the firstcamera might be “no, I see blue ball at (x1, y1) and a red ball at (x2,y2).” Feature encoding allows local processing of images by networkedcameras that share portions of the same view. For example, two camerasobserving the same room can communicate to one another the encodedfeature information of their respective views, and thereby construct a3D representation of objects in the room.

Referring to FIG. 20, a flow diagram illustrates a method for featureencoding that is based on raw data formatted as a 1D, 2D, or 3D array.The method illustrates that features can be represented as the extentsof groups of line segments.

A compact description of region features is efficiently collected fromthe data array. Block 2010 provides for collecting data line segments inraster order during Modified Propagate Last Labeling. Block 2020provides for grouping line segments according to Modified Propagate LastLabeling combined with other application requirements such as PrecisionFeature Labeling.

Block 2030 provides for representing features as the extents of groupsof line segments. Thus, the method is compatible with the raw dataformat and the CCL labeling methods disclosed herein and so is the mostefficient for hardware and software implementations. Further, a compactrepresentation of a feature is provided. For example, a color subregioncan be described as (red(xmin, ymin, xmax, ymax)).

Advantageously, the encoding is flexible. For example, the encoding iscompatible with 1D, 2D, and 3D spatial details as exposed by PropagateLast Labeling. Thus, the encoding produces a simple standard format forcommunicating feature information to other systems.

Referring now to FIG. 21, a digitized version of the encoded feature isillustrated. The bracketed (< . . . >) material such as label 2110,property 2120, and boundary 2130 represent numerical values illustratean encoding. For example the color “red” could be represented by thenumber “2.” In one embodiment, region properties vary according to theneeds of the application. For example, a banana-sorting machine needs todetermine yellow and green regions, while a golf-ball counting machineneeds to determine circular regions.

Referring now to FIG. 22 a diagram illustrates how data regionproperties and encoding are determined by the application requirements.An application can set up the segmentation rules and the appropriateproperty encoding so that the feature encoding function can extract andpackage the region features. More specifically, data 2210 is received byblock 2220 that applies segmentation rules to satisfy an application.Next, regions 2230 are output to feature encoding block 2240. Featurestructure 2250 is output to application 2260. Application 2260 interactswith both feature encoding block to determine feature propertiesencoding 2270. Also, application 2260 determines segmentationrequirements 2280.

Referring now to FIG. 23, a network example is illustrated. Compatibleapplications have compatible property encoding. As shown, Application 12310 can be networked to application 2 2320 and application 3 2330 via awired or wireless connection 2340. For example, Application 3 canrequest that App. 2 and App. 3 perform color segmentation and encode“red” as “2.” Thereafter all three applications can communicatefeatures. Also some standard property encodings may be previously knownby applications on the network.

Precision Features

Region features are captured as the segments arrive from either thesegmentation process or from an appropriate data source. The regionmaximum and minimum boundaries comprise the extents of the region,represented by a box showing the max-min boundaries, as shown in FIG.24. Region features are comprised of groups of segments, defined bysubextents. FIG. 24 illustrates that curved object 2410 can beinterpreted by features with a low precision 2420 resulting in a square,a medium precision 2430 resulting in a coarse object, or a highprecision resulting in a segmented version of the curved object 2440.Some features, such as shape, vary within the subextent. The larger thegroup of segments within a subextent, the more inaccurate the featurerepresentation as shown in 2440. But the smaller the extent, the moredata that must be recorded and transmitted to the receiving process.Therefore, the precision required of the features should determinesubextent size. Lower precision produces less feature data and a morecompact representation of a region. Higher precision allows a moreaccurate representation of the feature. For example, for textrecognition, the shape of letters will be important. For countingletters, only the full extent of each letter is required.

A “feature closed flag” is included in the feature structure. When thefeature is output to the receiving process, the flag signifies whetherthis is the last subextent for the feature. If the feature is closed,the next time a feature with the same label is output it will be a newand distinct feature. If the feature is not closed, the next featurewith the same label will be the next subextent for this feature.

Precision feature encoding consumes no extra memory because each featuresubextent is output when the precision threshold is reached. But the“feature closed flag” is not set. The next feature subextent is writtento the same feature structure with the same feature label.

Referring now to FIG. 25, a method for features is illustrated. Block2510 provides for collecting features one segment at a time and one lineat a time. Block 2520 provides for adjusting precision to enable databandwidth control. Block 2530 provides for rescanning data for featureinformation.

Disposed within block 2530 is block 25302 which provides for determininglabeled segments appropriate for transforming to determining additionalfeature information. Specifically, some data qualities may not becaptured during segmentation. Although data labels and regioninformation for segments are processed into features, the equivalent rawdata locations can be processed and the needed data qualities added tofeatures. For example, desired feature information for an image mayinclude complex texture qualities. The image data locationscorresponding to the labeled segments is rescanned and transformed tospatial/frequency coefficients and saved to the appropriate feature.

Note that raw data may be used in the segmentation process, so that somebuffering of the raw data could have occurred or be required inaccordance with system requirements. Precision features allow the userto adjust the data rate from CCL to suit the application. Motiondetection would only need large extents, which produce a low data rate.Object recognition may need smaller extents for more featureinformation, which would generate a higher data rate. Precision featuresand a variable data rate make this new CCL technology suitable as thepre-processor for the full range of host processors, from 8-bit embeddedmicroprocessors to 64-bit computers.

All of the functions described above can be linear and efficient insoftware and parallelizable for hardware for an embedded system.

While the subject matter of the application has been shown and describedwith reference to particular embodiments thereof, it will be understoodby those skilled in the art that the foregoing and other changes in formand detail may be made therein without departing from the spirit andscope of the subject matter of the application, including but notlimited to additional, less or modified elements and/or additional, lessor modified steps performed in the same or a different order.

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware and software implementations of aspects of systems; theuse of hardware or software is generally (but not always, in that incertain contexts the choice between hardware and software can becomesignificant) a design choice representing cost vs. efficiency tradeoffs.Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in standard integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies equally regardless of the particular type of signal bearingmedia used to actually carry out the distribution. Examples of a signalbearing media include, but are not limited to, the following: recordabletype media such as floppy disks, hard disk drives, CD ROMs, digitaltape, and computer memory; and transmission type media such as digitaland analog communication links using TDM or IP based communication links(e.g., packet links).

The herein described aspects depict different components containedwithin, or connected with, different other components. It is to beunderstood that such depicted architectures are merely exemplary, andthat in fact many other architectures can be implemented which achievethe same functionality. In a conceptual sense, any arrangement ofcomponents to achieve the same functionality is effectively “associated”such that the desired functionality is achieved. Hence, any twocomponents herein combined to achieve a particular functionality can beseen as “associated with” each other such that the desired functionalityis achieved, irrespective of architectures or intermedial components.Likewise, any two components so associated can also be viewed as being“operably connected”, or “operably coupled”, to each other to achievethe desired functionality, and any two components capable of being soassociated can also be viewed as being “operably couplable”, to eachother to achieve the desired functionality. Specific examples ofoperably couplable include but are not limited to physically mateableand/or physically interacting components and/or wirelessly interactableand/or wirelessly interacting components and/or logically interactingand/or logically interactable components.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of this subject matter describedherein. Furthermore, it is to be understood that the invention isdefined by the appended claims. It will be understood by those withinthe art that, in general, terms used herein, and especially in theappended claims (e.g., bodies of the appended claims) are generallyintended as “open” terms (e.g., the term “including” should beinterpreted as “including but not limited to,” the term “having” shouldbe interpreted as “having at least,” the term “includes” should beinterpreted as “includes but is not limited to,” etc.). It will befurther understood by those within the art that if a specific number ofan introduced claim recitation is intended, such an intent will beexplicitly recited in the claim, and in the absence of such recitationno such intent is present. For example, as an aid to understanding, thefollowing appended claims may contain usage of the introductory phrases“at least one” and “one or more” to introduce claim recitations.However, the use of such phrases should not be construed to imply thatthe introduction of a claim recitation by the indefinite articles “a” or“an” limits any particular claim containing such introduced claimrecitation to inventions containing only one such recitation, even whenthe same claim includes the introductory phrases “one or more” or “atleast one” and indefinite articles such as “a” or “an” (e.g., “a” and/or“an” should typically be interpreted to mean “at least one” or “one ormore”); the same holds true for the use of definite articles used tointroduce claim recitations. In addition, even if a specific number ofan introduced claim recitation is explicitly recited, those skilled inthe art will recognize that such recitation should typically beinterpreted to mean at least the recited number (e.g., the barerecitation of “two recitations,” without other modifiers, typicallymeans at least two recitations, or two or more recitations).Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” is used, in general such a constructionis intended in the sense one having skill in the art would understandthe convention (e.g., “a system having at least one of A, B, and C”would include but not be limited to systems that have A alone, B alone,C alone, A and B together, A and C together, B and C together, and/or A,B, and C together, etc.). In those instances where a conventionanalogous to “at least one of A, B, or C, etc.” is used, in general sucha construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, or C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.).

I claim:
 1. A method for propagated last labeling comprising: a.receiving one or more data files holding segmented data; b. setting amaximum number of available labels as a function of a number of labellocations on a current line of memory; c. identifying one or moresegments in the segmented data, the one or more segments sharing a sameregion; and d. labeling the one or more segments as a feature of thesame region, each feature representing a predetermined property of thesame region.
 2. The method of claim 1 wherein the receiving one or moredata files holding segmented data includes: receiving the one or moredata files as run-length encoded data and/or data encoded by auser-directed application.
 3. The method of claim 1 wherein the settinga maximum number of available labels as a function of a number of labellocations on a current line of memory includes: determining the numberof label locations on the current line of memory in accordance with auser-directed application and/or in accordance with a predeterminedfeature in the data file.
 4. The method of claim 1 wherein the setting amaximum number of available labels as a function of a number of labellocations on a current line of memory includes: determining the numberof label locations on the current line of memory so that the maximumnumber of available labels is equal to a required number features. 5.The method of claim 1 wherein the identifying one or more segments inthe segmented data, the one or more segments sharing a same regionincludes: identifying the one or more segments according to one or moreregion properties, the one or more region properties including a colorand/or a texture.
 6. The method of claim 1 wherein labeling the one ormore segments as a feature of the same region, each feature representinga predetermined property of the same region includes: labeling the oneor more segments as a feature using a label from a dedicated labelqueue, the maximum number of labels in use being equal to a number ofpossible label locations on the current memory line.
 7. The method ofclaim 1 wherein labeling includes: determining a maximum number ofunique segments on the current memory line; setting a number of possiblelabel locations as the maximum number; for each segment that does notconnect to a segment on the line below, closing that segment to enablereuse of an associated feature label and feature memory location; foreach segment that connects to a segment on a line below, and the segmenton the line below only connects to one segment, propagating the labelresulting in no net alteration in a number in use labels; and for eachsegment that connects to more than one segment on a line below,propagating a label for connected segments in the current line ofmemory.
 8. A computer program product comprising: a. a non-transitorycomputer readable medium configured to perform one or more acts forperforming propagated last labeling; b. the one or more acts comprising:i. one or more instructions for receiving one or more data files holdingsegmented data; ii. one or more instructions for setting a maximumnumber of available labels as a function of a number of label locationson a current line of memory; iii. one or more instructions foridentifying one or more segments in the segmented data, the one or moresegments sharing a same region; and iv. one or more instructions forlabeling the one or more segments as a feature of the same region, eachfeature representing a predetermined property of the same region.